As　a　kind　of　behavioural　characteristic,　keystroke　features　are　crucial　to　the　accuracy　of　user　identification　system　using　shallow　machine　learning　algorithms.　Filter　and　wrapper　feature　selection　algorithms　are　the　two　most　important　methods.　The　information　gain　and　particle　swarm　optimisation　algorithm　represent　the　two　feature　optimisation　methods,　respectively.　In　this　paper,　new　hybrid　binary　particle　swarm　optimisation　methods　combined　with　information　gain　theory　are　proposed　in　association　with　opposite-based　learning　and　distributed　techniques.　The　converted　information　gain　values　act　as　weight　coefficients　to　adaptively　adjust　the　flight　speed　of　particles.　The　support　vector　machine　(SVM)　algorithm　is　applied　to　evaluate　the　performance　of　feature　optimisation　in　terms　of　user　identification　accuracy　and　feature　reduction　rate.　Experimental　results　of　three　public　keystroke　datasets　show　that　the　proposed　optimisation　methods　achieve　better　classification　accuracy　with　fewer　features　than　four　existing　optimisation　methods.